Category Archives: Media coverage

The New York Times, whose editorial board has long been a strong supporter of the Affordable Care Act, published an article on its front page yesterday in which the headline read, “Seeking Rate Increases, Insurers Use Guesswork.” And, lest there be much doubt that the article suggested that speculation — the sort that regulators might understandably reject as a basis for premium hikes — rather than hard facts were leading to the frightening premium hikes, here are some quotes selected by author Reed Abelson for publication:

“But many insurers, including those seeking relatively hefty increases below 10 percent, say they are asking for higher premiums because they remain unsure about the future and what their medical costs will be.”

“It’s the year of actuarial uncertainty, and actuaries are conservative,” said Dr. Martin Hickey, chairman of the National Alliance of State Health CO-OPs and the chief executive of the New Mexico exchange. “The safest thing to do is to raise rates.”

Yes, to be sure, there was the suggestion in other parts of the article that higher than expected claims were part of the problem, but both the headline and remaining comments suggest that the high rates of increase were the result of unsupported speculation.

Wrong, New York Times! If you actually read the justifications for the premium increases submitted by insurers and their accompanying actuarial memoranda, you can see there are two dominant themes: (1) higher than expected claims expenses and (2) diminution of federal subsidies to the insurance industry. You can also see lengthy memoranda containing facts and figures explaining their experience last year and the basis for their trending those experiences into the future. And, while one need not invariably take the insurance industry at its word or at face value, this is an instance where they have to make the best case possible for their rate increases. Regulators will scrutinize insurers’ work. Misstatements or rank guessing would seem to be against the insurance industry’s interest.

So instead of quoting people, who might themselves be guessing, let’s look at what the insurers actually said. I am going to bore you with 17 representative filings from across the nation. I do so because I want to make clear that the evidence is overwhelming. Most of these are contained in or accompanied by lengthy memoranda containing elaborate tables justifying the increases. I’ve attempted to be diverse in my selection of insurers to avoid repetition of, for example, the Blue Cross position or the Aetna position.

1. Blue Cross and Blue Shield of Alabama

BCBSAL proposes an average 28% increase to rates for the products offered in 2015. The main drivers of the need for a rate increase are as follows:

• Single risk pool experience which is significantly more adverse than that assumed in current rates

• Expected increases in the average population morbidity of the Individual Market, also described in Section 5: Projection Factors

• Reinsurance program changes, described in Section 9: Risk Adjustment and Reinsurance

BCBSAL determined that the following items did not contribute significantly to the need for a rate increase:

• Taxes and fees: Minimal changes in the amount needed for taxes and fees, described in Section 10: Non-benefit Expenses and Profit & Risk

• Benefit changes: No changes to offered benefits for 2016

2. HealthNet of Arizona

The projected claims experience was developed using calendar year non-grandfathered 2014 experience. If our rate request is approved, the expected premium for the entire risk pool is $313.91 PMPM. This represents an increase of 24.7% in average premium. 2014 premiums received were $127,867,744. Claims paid were $171,764,569. Since 2014 medical costs are increasing with an annual trend of 5.5%. Prescription drug costs are increasing with an annual trend of 10.3%. Claims costs are 85.1% of premium. Administrative costs are 14.5% of premium. Profit is -4.8% of premium.

3. Cigna Health and Life Insurance Company (Connecticut)

The most significant factors requiring the rate increase are:

Changes in Medical Service Costs: The increasing cost of medical services accounts for the majority of the premium rate increases. Cigna anticipates that the cost of medical services in 2016 will increase over the 2015 level because of prices charged by doctors and hospitals and more frequent use of medical services by customers.

Transitional Reinsurance Program Changes: The federally mandated transitional reinsurance program is in effect for three years (2104, 2015, and 2016). The amount of funding available to issuers under the reinsurance program to offset adverse claim experience decreases each year ($10B in 2014, $6B in 2015, and $4B in 2016). Additional premium is required to compensate for the reduced reinsurance support in 2016.

Morbidity (Risk Pool) Adjustments: The marketplace for non-grandfathered individual plans is affected by provisions of the Patient Protection and Affordable Care Act (the Affordable Care Act) that became effective in 2014, including:
guarantee issue and renewal requirements
modified community-rating requirement
federal premium subsidies for low and moderate income individuals.

The effects of these 2014 changes when coupled with previous regulatory changes and overall utilization experienced in 2014 suggest that it is appropriate to increase the overall claim level assumption reflected in the premiums for individual plans in Connecticut.

4. Aetna Health, Inc. (Florida)

Why We Need to Increase Premiums
Medical costs are going up and we are changing our rates to reflect this increase. We expect medical costs to go up 10%. Medical costs go up mainly for two reasons – providers raise their prices and members get more medical care.
For policies issued to individuals in Florida, some examples of increasing medical costs we have experienced in the last 12 months include:
· The cost for an inpatient hospital admission has increased 8.0%.
· The average cost for outpatient has increased 8.4%.
· Costs for pharmacy prescriptions have gone up 8.0%.
· The use of outpatient hospital services has increased 4.5%.

What Else Affects Our Request to Increase Premiums
Several requirements related to the Affordable Care Act (ACA) impact these rates. These include:
· “Keep What you Have” and its impact on the population that will enroll in the plans covered by this filing
· Enhanced network access standards – which limit our ability to control the cost and quality of medical care
· Changes to required taxes and fees
· Phase-out of the Transitional Reinsurance Program which increases rates for plans issued to individuals

5. Humana Employers Health Plan of Georgia, Inc. (Georgia)

Many factors influence this rate calculation. The primary factors include
‐ Population health‐ Expected changes in the aggregate health level of all individuals insured by all carriers in the individual health insurance market.
‐ Claims cost trend‐ Changes in expected claims costs associated with changes in the unit cost of medical services, changes in Humana’s contracts with hospitals, physicians, and other health care providers, and the increase or decrease in utilization of medical services including changes in the severity and mix of services used.
‐ Plan Changes‐ Changes to plan designs due to changes in federal requirements.

6. Wellmark Health Plan of Iowa, Inc. (Iowa)

Reason for Rate Increases The effective average rate increase for these products is 28.7%, varying by plan as listed in the table above. The primary drivers of the proposed rate increases include, but are not limited to:

• Adverse Experience/Risk Adjustment Transfer: The risk of the market is more adverse than what we had assumed in the current rates; which leads to a significant projected risk adjustment transfer payment to other carriers.

• Phase out of Federal Transitional Reinsurance Program: As this program phases out over three years, the expected receivables from this program are smaller for 2016 than they were for 2015.

7. CareFirst of Maryland (Maryland)

The main driver of the financial performance of these products and the proposed rate increase is the very significant increase in average morbidity between 2013 (the pre-ACA pool which underwent underwriting) and 2014 (the post-ACA guarantee-issue pool). The allowed claims per member per month (PMPM) increased from $197 in 2013 to $391 in 2014, a much higher and faster increase than anticipated.

8. HealthPlus Insurance Company

The biggest driver of rate change is 2014 claims experience that is more adverse than assumed in current rates. Another driver is due to the lower Federal reinsurance recoveries.

9. Coventry Health & Life Insurance (Missouri)

Why We Need to Increase Premiums
Medical costs are going up and we are changing our rates to reflect this increase. We expect medical costs to go up 9.4%. Medical costs go up mainly for two reasons – providers raise their prices and members get more medical care.

What Else Affects Our Request to Increase Premiums
We offer individuals in Missouri a variety of plans to choose from. We are changing some benefits for these plans to comply with state and federal requirements.
Several requirements related to the Affordable Care Act (ACA) may also impact these rates. These include:
• Changes to our expected projected average population morbidity and its relationship to the projected market average for risk adjustment.
• Changes to required taxes and fees
• Phase-out of the Transitional Reinsurance Program which increases rates for plans issued to individuals

10. Aetna Health Inc. (Nevada)

Why We Need to Increase Premiums
Medical costs are going up and we are changing our rates to reflect this increase. We expect medical costs to go up 10.6%, excluding the effect of benefit changes described below. Medical costs go up mainly for two reasons – providers raise their prices and members get more medical care.

For Individuals in Nevada, some examples of increasing medical costs we have experienced in the last 12 months include:
• Primary Care Physician visits have increased by 124.2%.
• Inpatient bed days have increased by 51.0%.
• Expenses for emergency treatment have increased 22.7%.

What Else Affects Our Request to Increase Premiums
A prominent hospital system in Nevada moved from participating to non-participating in 2014 and is expected to stay that way into 2016. This has an adverse impact on claims costs since the more favorable lower-cost in-network reimbursement rates no longer apply.

Several requirements related to the Affordable Care Act (ACA) also impact these rates. These include:
• Enhanced network access standards – which limit our ability to control the cost and quality of medical care
• Changes to required taxes and fees
• Phase-out of the Transitional Reinsurance Program which increases rates for plans issued to individuals

11. Blue Cross Blue Shield of New Mexico (New Mexico)

[E]arned premiums for all non-grandfathered Individual plans during calendar year 2014 were $84,497,659, and total claims incurred were $105,605,811.

After application of the ACA federal risk mitigation provisions, the total BCBSNM Individual non-grandfathered block of business experienced a financial loss of 17% of premium in 2014.

The proposed rates effective January 1, 2016, are expected to achieve the loss ratio assumed in the rate development.

Changes in Medical Service Costs:

The main driver of the increase in the proposed rates is that the actual claims experience of the members in these Individual ACA metallic policies is significantly higher than expected. After application of the ACA federal risk mitigation provisions, the total BCBSNM ACA block of business experienced a loss of 19% of premium in 2014.

12. Medical Mutual of Ohio (Ohio)

Medical Mutual of Ohio is proposing an overall rate increase of 16.9% for plans effective January 1, 2016. This increase will potentially impact the 37,673 existing MMO members. The rate change ranges from 7.4% to 26.0%, varying by plan, age, change in tobacco user status, change in family composition, and the geographic area where the member resides.
The experience of MMO Individual ACA plans was not favorable in 2014. MMO has paid nearly $167 million claims and only received $114 million in premium. In 2014, MMO lost about $42 million dollars on its individual ACA business alone. With the rate increase implemented for 2015 and proposed for 2016, MMO’s experience is expected to improve, becoming profitable in 2016.
The following items are the main drivers for the proposed rate increase:
1. The transitional reinsurance recovery decreased from the 2015 level and will have a smaller impact offsetting the total claims.
2. The increase in the medical and drug cost is about 6.2% annually. Out of that increase, 40% is due to the change in unit cost, 31% is due to the change in utilization and the rest is due to the change in the mixture of services.
3. We expected the morbidity and demographics to improve in 2016 due to increased penalty of non-compliance, a greater understanding of the ACA law, and a reduction in the amount of pent-up demand for services. This alleviates the rate increase needed based on the experience.
4. There’s no changes in benefit from 2015 to 2016.
5. The administrative cost and commission will decrease $2.51 per member per month. The profit and risk will increase $7.92 per member per month. The taxes and fees will increase $4.51 per member per month.

13. Geisinger Quality Options (Pennsylvania)

Geisinger Quality Options has proposed an overall base rate increase of 58.36% for Individual PPO members renewing in the Marketplace effective January 1, 2016 through December 1, 2016. The overall increase is largely due to the claims experience in ACA compliant individual market plans being much higher than what was assumed in current rates. Other contributing factors include annual claims trend, federally-prescribed ACA fees and reduced benefits in the Transitional Reinsurance Program.

14. Pacific Source Health Plans (Oregon)

This filing requests an aggregate increase of 42.7 percent over the rates approved in our 2015 Oregon Individual filing. The proposed rates are based on PacificSource’s historical Oregon Individual claims experience adjusted for PacificSource’s historical average risk compared to the market average risk, anticipated medical and pharmacy claims trend, expected change in market morbidity from 2014 experience period to 2016 projection period, changes in benefits, and expected state and federal reinsurance recoveries. The proposed rates also reflect changes in the taxes and fees imposed on health insurers for 2016. The range of rate increases is 23.4 percent to 60.4 percent and impacts PacificSource’s 8,216 Oregon Individual members. The variation in rate increases is driven by some changes in benefits i.e. copays, deductibles, OOP max, as well as adjustments to geographic area factors. The overall average impact of benefit changes on the requested rate increase is 0.0 percent.

The increase in rates from 2015 to 2016 is primarily driven by a dramatic worsening of claims experience in 2014 as compared to 2013, and the reduction of expected reinsurance recoveries in 2016. Note that this is the first rate filing where a full year of post ACA experience data was available. This data shows that the overall increase in morbidity from PacificSource pre ACA experience to post ACA market experience is much greater than originally projected in our 2014 and 2015 rate filings. The combined medical and pharmacy annual trend used in this filing is 7.0 percent, which reflects expected changes in costs, changes in utilization, and the impact of leveraging. The primary driver of the annual trend assumption is specialty drug cost and utilization, particularly Hepatitis C drugs. Administrative expenses and margin are budgeted to decline compared to the 2015 rate filing.

Over the calendar year 2014, the Oregon Individual block earned 30.2 million in premium and incurred an estimated 50.0 million in claims, for a raw medical loss ratio of 165.2 percent. Premium and claims expenses are shown before the impact of reinsurance, risk adjustment, and risk corridor. At this time we do not expect risk corridor payments to be made to issuers. After expected risk adjustment and state and federal reinsurance recoveries, we estimate a 2014 loss ratio of 116.5 percent. Combined administrative expenses, commissions, taxes, and assessments were approximately 24.6 percent of premium.

15. Scott & White Health Plan (Texas)

The Scott & White Health Plan is requesting an average rate increase of 32.3% to the Individual HMO Rating Pool. There are 24,294 covered individuals as of January 2015. 10.0% of the 32.3% increase is due to health care cost inflation, 14.3% of the increase pertains to changes in Risk Adjustment and Reinsurance assumptions, 2.7% is due to changes in fees, and the remaining 5.3% is due to actual and expected unfavorable experience.

16. Optima Health Plan (Virginia)

The rate increase is the same for all members in the same plan. Where the 2016 plan is different than the 2015 plan these members will be automatically enrolled into the 2016 plan shown. Premium rates are effective January 1 2016.
Claims expenses were very high in 2014 relative to earned premium. However payments from the federal transitional reinsurance and risk adjustment programs are expected to help significantly.
The federal reinsurance program is only temporary and while it is continuing into 2016 the amount of reinsurance per claim is less than in 2014 and 2015. As such premium rates will be increased to account for this impact. Additionally the risk adjustment program alone does not appear to provide sufficient relief to enable the Company to meet its pricing targets.
It is anticipated that 2014 had some amount of higher claims due to new members having pent-up demand for services and less healthy people tending to be the first to sign-up for ACA-compliant plans given the new rating and underwriting rules. Because of this we do not assume that 2016 will necessarily be as high a claim level as seen in 2014 but some of what has been experienced will remain.
These reductions from 2014 levels will be countered by upward pressure on costs from other sources such as medical trend as described below.
The proposed rate increase is intended to account for expected claims activity in 2016 given historical experience and changes in morbidity as well as any expected assistance from the federal reinsurance and risk adjustment programs. With the proposed rate increase the anticipated loss ratio is 80 percent.
Medical trend for these products is anticipated to be an average of 7 percent per year on paid claims for example after member cost sharing or a total of 14.5 percent over the period from 2014 to 2016. This was developed based on historical experience as well as consideration for information available on general medical inflation trends. Medical trend includes a combination of utilization and costs of services. This increase in cost is included in the calculation of the rate increase.

17. Security HealthPlan of Wisconsin (Wisconsin)

The biggest driver of the rate change is SHP’s underlying claims experience used in developing the projected index rate. We used SHP’s 2014 individual non-grandfathered, ACA allowed claims as the basis for claim development. The 2014 claims and membership distributions indicate experience is worse than we priced for in 2015 rates. Further, based on a Wisconsin risk score analysis conducted by Milliman, we are projecting no risk adjustment transfer payment. This assumption of no payment results in higher rates in 2016 since we had projected SHP would receive money from the risk adjustment pool when developing the 2015 rates.

Another driver of the rate change is due to the lower federal transitional reinsurance recoveries in 2016. The recoveries assume in 2016 SHP will receive 50% of all SHP’s individual members’ per member per year incurred claims between $90,000 and $250,000. In 2015, rates were priced assuming recoveries to be 50% of claims between $70,000 and $250,000 based on the federal parameters in place at the time of pricing.

The projection of claims from the experience period to the effective period assumes 5.0% annual medical and drug trend. These trends were estimated based on data from SHP, conversations with SHP senior management, Milliman research, general industry knowledge, and our judgment of recent trends.

Conclusion

So, does this sound like “guesswork” to you? It does not to me. All of these insurers are lying or mistaken about what is causing their requests for premium hikes? I don’t think so. Of course, there is “trending” in which insurers approximate how previous increases will continue to the future and this requires some art on the part of insurers. Of course, insurers may want to present their requests for rate hikes in a way more likely to be approved. But what they have presented is no more “guesswork” here than the work of any insurer in setting rates for almost any form of insurance. It is the sort of actuarial projections that are generally approved by regulators.

Health insurers now have a decent feel what it is going to cost them to participate in Obamacare. And these insurers have a pretty common perspective: the whopping increase are driven by greater utilization than expected among those electing coverage (adverse selection and moral hazard), increases in the cost of medicine, and reduction of federal subsidies.

In theory, open enrollment ends tonight. No longer can individuals without any excuse whatsoever wait to purchase health insurance policies on the Exchanges that will be in effect during 2014. I very much expect we will hear numbers over the next few days such as 6.5 million or 7 million bandied about as “enrollment figures.” Many supporters of the ideas behind the original ACA who have managed to tolerate its metamorphosis over the past year will herald those numbers as signs of success. And, indeed, those numbers are considerably better than many had feared. I, for one, am prepared to confess that I may have been too pessimistic in the past about first year enrollment in the ACA. My pessimism is all the more glaring because the enrollment numbers are apparently coming notwithstanding the Obama administration’s decision to tie its hands behind its back by creating a new opportunity to evade the individual mandate via an undocumented “hardship exemption” and to delay making a purchase decision based on “honor system” claims of difficulties in accessing healthcare.gov or state enrollment systems.

That said, however, these enrollment figures are essentially irrelevant. Those who persist in touting them as signs of success reveal themselves more as Obamacare fanboys than as credible advocates or serous scholars. The aggregate enrollment figures are irrelevant for two reasons and of lesser value for another. In short, my predictions may have been on the low side, but the numbers that will be coming out tonight absolutely do not vindicate those who assured us that Obamacare would end up being just fine. Reciprocal honesty would be nice.

Nothing economically or legally in the ACA turns on “enrollment”

1. “Enrollment” is, as many have noted, simply checking boxes on a web site. It is not the same thing as actually having a policy in force, of being covered by an insurer who will pay medical bills in the event the “insured” becomes sick. In terms of the real health of Americans, access to expensive medical care, enrollment numbers are as meaningful to the success of the ACA as knowing the number of individuals outfitting their fantasy Corvette Stingray on Chevrolet’s“build your own” web site is to the success of GM. The former is a leading indicator of interest in plans on the Exchanges just as the latter is a possible leading indicator for purchasing a snazzy car. But that, really, is all. Responsible journalists and bloggers should stop bleating “enrollment” merely because it is the only number the Executive branch dispenses.

Friends of the Obama administration gain credibility and actually help their President by insisting that the Obama administration release the number of policies in force This is so because there are only two and a half conclusions that can be drawn from silence: either the Obama administration has the number and is refusing to provide it or it just doesn’t have the number. Not releasing a number or decent approximation is contrary to the transparency values that the Obama administration espouses. My FOIA request on the topic is, like many others, I suspect, unanswered. Not having the number is perhaps even worse. I have read much of the ACA and its regulations and I can not think of a single economic or legal matter that turns on the number of people who have “enrolled” in plans on the Exchange. Insurers get paid, tax credits are advanced, and a whole host of other important financial and legal consequences depend instead on the number of people who, for any given month, actually have coverage, have a policy in force. I say “two and a half” conclusions because there is a variant of not having the information that is sometimes advanced — not having precise enough information to release. But this is about as flimsy as it gets. Governments release approximate numbers all the time. I believe Americans are sophisticated to understand the word “about” and the concept of a “good faith estimate.” Any lack of precision down to a single individual does not excuse a failure to release relevant information. I am confident that the public would be well served by having a policies in force number that was accurate even to just two or three significant digits.

The difference between enrollment and purchase is not trivial. Suppose the fall off between enrollment and purchase is, as some have suggested, 20%. And suppose further, as again some have said, that 3% of the remaining policyholders don’t pay each month. The graph below shows the fraction of policyholders persisting over 12 months. The blue line shows the time series and the yellow dotted line shows the average level of policies in force. As one can see, by the end of 9 months, only 65% of the initial level of policies remain in force. The annual average is about 70% of the starting amount. Moreover, the healthy are the most likely to stop paying; those who are sick are most likely to persist in their policies. Thus, if 6.5 million enrollees start out, only 4.2 million will be left with policies at the end of 9 months and the average number of purchasers over the year will be 4.6 million.

Even if just 10% decline to pay their first month’s bill, only 70% of the policies remain in force at 9 months (see figure below on left); the annual average is about 79%. At 20% initial decay and 5% monthly decay thereafter, only about 55% remain after nine months; the annual average is 65% (see figure below on right). And some, of course, have suggested the fall off between enrollment and retention of a policy is actually worse.

Note: it is possible that some of the losses due to non-payment will be offset by those purchasing policies via “special enrollment.” These individuals, however, may be particularly high risk.

Aggregate purchases or, worse yet, aggregate enrollment is largely irrelevant to the success of the ACA. There are at least 51 markets for individual policies. The fact that California may have exceeded expectations in terms of purchases does nothing to help Texas, Louisiana, Arizona and other states where enrollment has been low. Insurers in states where enrollment is low or the demographics are particularly problematic — few young people, lots of middle age women — will not be compensated for their losses by the fact that enrollments are better in California, New York and Connecticut. Even if it is the same parent company that makes money in one jurisdiction, that will not deter the subsidiaries in losing jurisdictions from either withdrawing or raising premiums. There will still be immense pressure on insurers in the less successful states to either drop out entirely — something the make-it-up-as-you-go-along implementation of Obamacare fosters — or to raise prices substantially.

As I said in a story broadcast on National Public Radio and as the New York Times admits, we need to stop thinking about the ACA as a single narrative and start to think about it as multiple complex narratives. Indeed, it’s probably more like 175 or more narratives (the number of issuers in all federal plans) because not only will the experience vary between states, they will also vary by insurer. An insurer who charged a very low premium in a given state and attracted a good deal of business may react very differently next year in pricing policies than an insurer even in the same state who charged a high price and got less.

3. Experience matters most

But the life of the Affordable Care Act will not be enrollment or even purchases, it will be experience. Did insurers set prices realistically or did they underestimate the medical problems and demand for services of enrollees? Does the fact that many of the purchasers on the Exchange actually had policies already actually help insurers because many of those purchasers would have undergone at least some recent medical underwriting? Are the networks that have been created by insurers so narrow that they will lead to unsatisfactory medical care or will they in fact keep prices low? Will insurers regard uncertainty in the political environment — like not knowing whether Obama will extend the inchoate hardship exemption into next year when, otherwise, the individual mandate/tax/penalty more than doubles — result in at least some insurers pulling out of the market? Will the diminishing reinsurance available to insurers writing in the Exchanges have the effect I predicted of increasing prices by about 7%?

Conclusion

Tonight, March 31, is a milestone for the ACA, but it is hardly the end of the challenge. We’ll see lots more important data start to dribble in over the next few months, including, critically, information on premiums insurers hope to charge in 2015. In the interim, though, could everyone please start to focus on statistics and data that matters rather than proxies such as “enrollment.” Use of enrollment rather than policies in force may at one time have been a necessary evil. But persisting in doing so is a practice that is no longer useful other than as a vehicle for spreading political propaganda.

My suggestion, only slightly tongue in cheek, is that, just as the IRS imputes some awful income to you if you don’t file a tax return, journalists and bloggers start reporting appallingly low purchase numbers until the Obama administration releases the actual data. I’ll start:

“Tonight’s disclosure by the Obama administration that about 4.5 million individuals will have coverage via Exchanges under the Affordable Care Act during 2014 means that some big states such as New York and California have done reasonably well in making it likely that their insurance markets will be stable. It also means, however, that Exchanges could be under great stress in a number of states, most notably Texas.”

Addendum

4/7/2014

My rough estimate of the rate of decline in Obamacare enrollment appears to be vindicated by an article appearing here in Kaiser News titled “Why Some Don’t Pay Their Obamacare Premium: It’s Not What You Think.” Kaiser reports that Covered California, the Exchange for the nation’s largest state, has produced a report projecting a significant drop in the number of enrollees throughout the year:

According to the report between 53 and 58 percent of Covered California enrollees are expected to stay in a Covered California plan for 12 months. This analysis is consistent with a Kaiser Family Foundation study published earlier this year. It found that of people who enrolled in an individual insurance plan in 2010, years before the health law fully kicked in, only about 48 percent were still in the individual market two years later. (Kaiser Health News is an editorially independent program of the foundation.)

But most of these people dropping ACA Exchange coverage won’t become uninsured, the report says. Instead, they will go on to the state’s expanded Medicaid program or find better/cheaper coverage elsewhere. It’s not clear from the Kaiser article or the Covered California report whether they expect those moving out of the Exchanges to be healthier than average. In any event, though, the report further establishes why touting “enrollment” is ridiculous.

On December 17, 2013, the Kaiser Family Foundation published an influential study that comforted many supporters of the Affordable Care Act who had been made nervous by early reports that the proportion of younger persons enrolling in Exchanges was significantly less than expected. If true, such a disproportion could have created major stress on future premiums in the Exchanges because the private Exchange system under the ACA depends — or so it was thought — on younger persons subsidizing older persons. The Kaiser study asserted, however, that even if one cut the number of younger persons by 50%, insurer expenses would exceed insurer premiums by “only” 2.4%. This finding under what it thought was a “worst case scenario” underpinned Kaiser’s conclusion that a “premium death spiral was highly unlikely.”

This post evaluates the Kaiser analysis. I do so in part because it disagreed a bit with my own prior findings, in part because it has gotten a lot of press, and because I have had a great deal of respect for Kaiser’s analyses in general. I conclude that this Kaiser analysis rests, however, on an implausible assumption about the behavior of insurance purchasers and lacks much of a theoretical foundation. Once one eliminates this implausible assumption and employs a better theory of insurance purchasing, the threat of a death spiral becomes larger.

The reason for all this is a little complicated but try to bear with me and I will do my best to explain the problem. Essentially, what Kaiser did was to run its simulation simply by lopping off people under the age of 34 and assuming that, for some reason, the disinclination of people to purchase health insurance on an Exchange would magically stop at age 34. Thus, if an enrollment of, say, 2 million had been projected to come 800,000 from people age 18-34, 600,000 from middle aged people and 600,000 from the oldest group of enrollees, the “worst case” scenario Kaiser created (Scenario 2) would reduce enrollment to 1.6 million by having 400,000 come from people age 18-34, 600,000 from middle aged people, and 600,000 from the oldest group of enrollees. Thus, the youngest group would now constitute 25% of enrollees rather than 40%, and the other groups would constitute 37.5% of enrollees rather than 30%.

Although there is often nothing automatically wrong with this sort of “back of the envelope computation” — I have done many of them myself — sometimes they give answers that are wrong in a meaningful way. And sometimes “meaningful” means a difference of just a few percentage points. Thus, although the difference between 0.045 and 0.024 is not large on an absolute scale, this is one of these instances in which there could be a big difference between predicting premium increases augmented by 2.4% due to this particular form of adverse selection and predicting a premium increases augmented by 4.5% due to this particular form of adverse selection. The first might be too small to lead to a quick adverse selection death spiral; the second, particularly if it combined with other factors increasing premiums, might be enough to start a problem. Death spirals are a non-linear phenomenon a little like the “butterfly effect” in which small changes at one point in time can cascade into very large changes later on. What I feel comfortable saying is that the additional risk of a death spiral created by disproportionate enrollment of the an older demographic is greater than Kaiser asserts.

By simply lopping off the number of people under 35 who would enroll, the Kaiser model lacked a good theoretical foundation. The model Kaiser should have run — “Scenario 3” — is one in which the rate of enrollment is a sensible function of the degree of age-related subsidy (or anti-subsidy). Their two other scenarios could then be seen as special cases of that concept. Had they run such a “Scenario 3”, as I will show in a few paragraphs, the result is somewhat different.

Let me give you the idea behind what I think is a better model. I’m going to present the issue without the complications created by the messiness of data in this field. We need, at the outset to know at least two things: (1) the number of people of each age who might reasonably purchase health insurance if the subsidy were large enough (the age distribution of the purchasing pool); and (2) the subsidy (or negative subsidy) each person receives for purchasing health insurance as a function of age. By subsidy, I mean the ratio between the expected profit the insurer makes on the person divided by the expected expenses under the policy, all multiplied by negative one. The bigger the subsidy, the more money the insurer loses and the more likely the person is to purchase insurance.

Suppose, then, that the probability that a person will purchase health insurance is an “enrollment response function” of this subsidy. For any such enrollment response function, we can calculate at least three items: (1) the total number of people who will purchase insurance; (2) the age distribution of purchasers (including the “young invincible percentage” of purchasers between ages 18 and 35); and (3) — this is the biggie — the aggregate return on expenses made by the insurer. Thus, some enrollment response function might result in 6.6 million adults purchasing insurance of whom 40% were “young invincibles” that generated a 1% profit for the insurer on adults while another enrollment response function might result in 2.9 million adults purchasing insurance of whom 20% were “young invincibles” that generated a 3% loss for the insurer on adults.

What we can then do is to create a family of possible enrollment response functions drawn from a reasonable functional form and find the member of that family that generates values matching the “baseline assumptions” made by both Kaiser and, apparently, by HHS about total enrollment and about the “young invincible percentage.” We can then calculate the aggregate return of the insurer on adults and call this the baseline return. What we can then do is assume different total enrollments and different young invincible percentages, find the member of the enrollment response function family that corresponds to that assumption, and then calculate the new revised return on adults. The difference between the baseline return and the new revised return on adults can be thought of as the loss resulting from this form of adverse selection. There are a lot of simplifications made in this analysis, but it is better, I believe, than either the back of the envelope computation by Kaiser that has gotten so much press and, frankly, the back of the envelope computation I did earlier on this blog.

Here’s a summary of the results. When I (1) use the Kaiser/HHS age binning of the uninsured and indulge the simplifying assumption that the age distribution is uniform within each bin; (2) use Kaiser’s own estimate of the subsidy received by each age, (3) assume 7 million total purchasers ; and (4) assume 40% young invincibles with uniform age distribution within age bins, I find that the baseline return on adults is 1.0%. When I modify assumption (3) to have 3 million total purchasers and, as Kaiser did in Scenario 2, modify assumption (4) to have 20% young invincibles, the baseline return on adults is -3.5%. Thus, a better computation of Kaiser’s worst case scenario is not a reduction in insurer profits of 2.4%, but rather a reduction of 4.5%.

The graphics here compare enrollment rates, the age distribution of enrollees and various statistics for the baseline scenario and the scenario in which there are 3 million total purchasers and approximately 20% young invincibles.

Various scenarios showing changes in insurer profits due to different enrollment response functions

Please note that the computations engaged in here essentially ignore those under the age of 18. This is unfortunate, but I do not have the data on the expected premiums and expenses of children. It does not look as if Kaiser had that data either. Since children are expected to comprise only a small fraction of insured persons in the individual Exchanges, however, this omission probably does not change the results in a major way.

A humbling thought

The more I engage in this analysis, the more I realize how difficult it is. There are data issues and, more fundamentally, behavioral issues that we do not yet have a good handle on. Neither my model nor Kaiser’s model can really explain, for example, why, as has recently been noted, enrollment rates are so much higher in states that support the ACA by having their own Exchange and with Medicaid expansion than in states that more greatly oppose the ACA. As I have suggested before, there is a social aspect and political aspect to the ACA that is difficult for simple models to capture. Moreover, as I noted above, this is an area where getting a number “close to right” may not be good enough. Premium increases of, say, 9% might not trigger a death spiral; premium increases of 10% might be enough. And neither my nor anyone else’s social science, I dare say, is precise enough to distinguish between 9% and 10% with much confidence.

So, longer though it makes sentences, and less dramatic as it makes analyses and headlines, the humbling truth is that we can and probably should engage in informed rough estimates as to the future course of the Affordable Care Act, but it is hard to do much more as to many of its features. I wish everyone engaged in this discussion would periodically concede that point.

Other Problems with Kaiser

There are other issues with the Kaiser analysis. Let me list some of them here.

Even accepting Kaiser’s analysis premium hikes would likely be more than 2%

Kaiser’s discussion of insurer responses to losing money is inconsistent. Look, for example, at this sentence in the report: “[i]f this more extreme assumption of low enrollment among young adults holds, overall costs in individual market plans would be about 2.4% higher than premium revenues.” Kaiser further reports “Insurers typically set their premiums to achieve a 3-4% profit margin, so a shortfall due to skewed enrollment by age could reduce the profit margin of insurers substantially in 2014.” I don’t have a quarrel with this sentence. But then look at what the Kaiser report says. “But, even in the worst case, insurers would still be expected to earn profits, and would then likely raise premiums in 2015 to make up the shortfall,” No! According to Kaiser’s own work, “even in the worst case,” insurer costs would be 2.4% greater than premium revenues. Since there is little float in health insurance and investment return rates are low these days, insurers would likely not earn profits. Then it gets worse. “However, a one to two percent premium increase would be well below the level that would trigger a “death spiral.” Perhaps so, but if insurers need to earn 3-4% to keep their shareholders happy and they are losing 1-2%, a more logical response would not be a 1-2% increase in premiums but a 4-6% increase. And, as Kaiser points out, larger premium increases could trigger a premium death spiral in part because death spirals are like avalanches: they start out small, only a little snow moves, but once the process starts it can become very difficult to abort.

Logical Fallacies

The first paragraph of Kaiser’s report asserts: “Enrollment of young adults is important, but not as important as conventional wisdom suggests since premiums are still permitted to vary substantially by age. Because of this, a premium “death spiral” is highly unlikely.” Even if the first sentence of this quote were correct — a point on which this entry has cast serious doubt — the second sentence does not follow. To use a sports analogy, it would be like saying that, the role of a baseball “closer” is important but not as important as conventional wisdom suggests. Therefore the Houston Astros, who lack a good closer, are highly unlikely to lose. No! There are multiple factors that could cause an adverse selection death spiral. Just because one of them is not as strong as others make out, that does not mean that a death spiral is unlikely. That’s either sloppy writing or just a pure error in logic.

Other Factors

And, in fact, if we start to look at some of those other factors, the threat is very real. As discussed here in more depth, I would not be surprised if adverse selection based on completely unrated gender places as much pressure on premiums as adverse selection based on imperfectly rated age. And, as I have discussed in an earlier blog entry, the transitional reinsurance that somewhat insulates insurers from the effects of adverse selection will be reduced in 2015. This will place additional pressure on premiums.

And, on the other hand, the individual mandate, assuming it is enforced, will triple in 2015 and risk adjustment measures in 42 U.S.C. § 18063, will likely provide greater protection for insurers. These two factors are likely to dampen adverse selection pressures.

Notes on Methodology

There are a number of simplifying assumptions made in my analysis. Some of them are based on data limitations. Here are a few of what I believe are the critical assumptions.

1. Functional form: I experimented with two functional forms, one based on the cumulative distribution function of the logistic distribution and the other based on the cumulative distribution function of the normal distribution. These are both pretty conventional assumptions and make sure that the enrollment rate stays bounded between 0 and 1. The results did not vary greatly depending on which family of functions the enrollment response functions were drawn from.

2. Uniform distribution of ages within each age bin of potential purchasers. I believe this is the same assumption made by Kaiser and it results from the absence of any more granular data on the age distribution of the uninsured that I was able to find.

3. The enrollment rate depends on the subsidy rate standing alone and not other possibilities such as subsidy rate and age. The data on enrollment rates is very sparse and so it is difficult to use very complex functions. Perhaps a more complex analysis would assert that enrollment depends on both subsidy rate and age, since age may be a bit of a proxy for the variability of health expenses and thus of risk.

Request for readers

1. The healthcare.gov web site does not even let one access Catastrophic plans if one is over the age of 30. The shadow website, thehealthsherpa.com does not permit one to do so either probably because it was thought that the number of persons qualifying over 30 would be extremely small. When I just tried to chat with healthcare.gov and get the answer, I was told ” Sorry, Health Insurance Marketplace Live Chat isn’t available right now. We’re having technical problems.” I have thus not yet been able to figure out what the prices are for someone over 30 with one of the new “hardship” exemptions. If anyone can figure out what prices over-age hardship exemption folks pay for a section 1302 Catastrophic Plan, please contact me.

2. How many people have purchased these Catastrophic plans anyway? The federal government has not released metal tier distribution data, but the data from a few states suggests that it is an extremely low number. Many under the age of 30 can stay on their parents plans and others find that it is not much better than a Bronze Plan or, under some circumstances, a worse deal. I would bet that the overall number of Catastrophic Plan enrollees thus far is less than 20,000. There is no subsidy for Catastrophic Plans. What would actually happen if people took advantage of the Secretary’s hardship exemption and instead of just pocketing the tax savings, these older insureds purchased these Catastrophic Plans. Could be the over 50s could end up being a greater number than the under 30s. We will see if I ever get an answer to question 1 above, but I suspect the insurance industry did not price the policies on the assumption that older enrollees would predominate.

The Obama administration released critical data yesterday on the aggregate levels of enrollment in the various individual Exchanges. Most of the journalistic and blogospheric effort in the aftermath has been in trending: do these numbers portend a massive leap forward in Exchange enrollment such that there can be some confidence that the Affordable Care Act will in fact work? Might this alternatively be some sort of temporary surge that is both too little and too late? All of this analysis is completely fine; I’ve engaged in it myself. But there are other issues that should be examined.

Here are five questions, mostly about data, I’d like to see other journalists or bloggers start to pursue. I’m doing some of it myself, but I would love company.

1. What is the distribution of enrollment among the various metal tiers?

If a lot of people are purchasing the gold and platinum plans, that is a sign that the people signing up have poor health and do not want to pay higher deductibles. This is particularly true if the same pattern exists among the enrollees receiving income-based subsidies: they, after all, are mostly purchasing gold and platinum because they need it, not because it easily accommodates their budget. If, on the other hand, the distribution is weighted towards the bronze and silver plans, that is some evidence that the people signing up may not be coming as disproportionately from the low or middle expense range. Unless one’s funds are very limited, it does not make sense for someone who knows they will have high medical expenses to purchase a bronze plan. Disproportionate purchase of gold and platinum policies heightens the potential for adverse selection problems to the extent insurers believed the federal government’s models, which assumed only mild “induced demand” for such policies.

Journalists should also continue pressing at the state and federal level for information on age distribution of enrollees; I can see no legitimate reason to withhold it.

2. What is the income distribution of those purchasing policies on the Exchanges? Is the ACA turning into welfare for the wealthy?

The report released by HHS on December 11, 2013, had an important statistic buried on page 7. I’ve highlighted it in the screenshot below.

Page 7 of the HHS Report

Whoa! Only 41% of those purchasing policies are getting subsidies. The original Congressional Budget Office projections (see Table 3 of this Report) indicated that only 1 million of the 7 million enrollees (14%) would have incomes that high. This means 59% of the purchasers have household incomes in excess of 400% of the federal poverty level. Is the Affordable Care Act another form of welfare for the wealthy?

I’m working on some computations for a future blog entry, but my initial sense is that the data so far probably means that the sign up rate among those in the middle class living in families earning between 139% and 400% of federal poverty level is less than a third the sign up rate among the wealthier. There are more people with incomes in the 139% to 400% group than the 400+% group (look here); there are certainly considerably more people without insurance in the former group than in the latter (look here, in Table 1). It further means, by the way, that, so far, the individual Exchanges on the Affordable Care Act, with all of their overhead costs and all of the rhetoric expended on them, have probably helped fewer than 100,000 people in the middle class to date. Most of the prospective beneficiaries thus far are in upper income groups for whom a simpler system might have been all that was needed. Anyway, because distributional concerns are relevant to an evaluation of the Affordable Care Act, data on income distribution of enrollees is important.

By the way, for the reported cost of the fancy healthcare.gov website, $600 million, one could just have had a lottery and given a Silver or Gold policy to 100,000 middle class people for a year and probably had some change left over.

3. What is the distribution of enrollment by price ranking among the various plans?

If almost everyone is selecting the low-cost plans within each metal tier, what happens to the many more insurers who wrote plans with higher prices? If those insurance plans have high prices because they have bigger and better networks, those insurers will be concerned that they have attracted a few really, really expensive insureds who want to take advantage of that possibility. Plus, are there insurers who have just said that the administrative costs of insuring a tiny population are not worth whatever upside there may be? I bet there are insurers in many states that to date have fewer than five enrollees in at least some of their plans. Given that nine states (Alaska, Delaware, Iowa, Mississippi, New Mexico, North Dakota, South Dakota, West Virginia and Wyoming) have fewer than 1,000 enrollees to date and multiple tiers with multiple plans, that almost has to be the case. Are the “losers” in the Exchange thinking, therefore, of pulling out in the future?

4. What is enrollment in the SHOP Exchanges?

One of the forgotten but important features of the ACA is the ability of small business (<50 for 2014) to purchase group insurance for their employees and dependents in state or federally facilitated Exchanges (“SHOP Exchanges”). Insurers inside and outside of the SHOP Exchanges will no longer be able to rate based on the health or medical claims records of the insureds or surrogates for it such as gender. They will be able to rate only on the basis of the region, the employer’s mixture of employee ages and, in theory, employee tobacco use. (42 U.S.C. § 300gg). The area may have been forgotten because healthcare.gov, the federal website that was supposed to facilitate selection of plans by small employers and subsequent enrollment, abandoned efforts to get the small business portion of its website to work once the individual site failed. But enrollment is still supposed to be occurring by paper at the federal level and through various means in the various state exchanges.

Let’s get some data! How many small businesses have enrolled? Are employers enrolling? How many employees are selecting coverage? How many employers are taking advantage of substantial tax credits to purchase this insurance under section 45R of the Internal Revenue Code? Are insurers concerned that the employers who are enrolling in these health-unrated plans will tend to be those who have a problematic risk profile among their employees and that adverse selection could start to deprive small business of the opportunity the ACA purported to grant.

5. Are insurers in fact “uncanceling” policies where they have been permitted to do so and is anyone buying them?

One of the fears that I and many others raised following President Obama’s “about face” and decision not to prohibit renewal for one more year of certain insurance policies that did not provide Essential Health Benefits or comply with certain other provisions of the ACA was that such a move could destabilize the insurance markets. To make a long story short, if state insurance commissioners permitted the “uncancellations” and insurers then “uncancelled,” insurers selling plans in the Exchange based on a population that included those mostly healthy people would likely lose more money. We now know that many but not all states are permitting insurers to uncancel. What I have not seen data on, however, is how many insurers are taking advantage of the opportunity and how many insureds are accepting offers by these insurers to renew. The larger this number, the greater the threat to insurance sold through the Exchanges or the greater the hit to the Risk Corridors program to make the insurers whole. On the other hand, if few insurers are actually taking advantage of the opportunity and/or few consumers are re-upping, the hypothesized threat to the insurance Exchanges will have been reduced and the number of people potentially hurt by violation of the “if-you-like-your-health-plan-you-can-keep-your-health-plan” pledge will have been kept large.

Conclusion

Getting all this data is likely to be difficult. The Obama administration appears extremely sensitive to release of any data that could diminish confidence in the ultimate success of the Affordable Care Act. Moreover, some of the data will need to come from insurance companies who have different disclosure obligations than do the federal government and state governments. Still, the questions are important and neither journalists nor the public should ever confine themselves to just the information government is willing or eager to disclose.